Extreme-value analysis in nano-biological systems: Applications and Implications

TL;DR

This paper reviews the application of extreme value analysis (EVA) to nanoscale biological systems, emphasizing its potential to reveal insights from extreme data points in complex biological environments.

Contribution

It introduces EVA concepts to biology, reviews current applications in motor protein studies, and advocates for broader use of EVA in life sciences.

Findings

EVA can extract physical properties of motor proteins in vivo

Limited current applications of EVA in biology exist

EVA has potential to uncover hidden properties in biological data

Abstract

Extreme value analysis (EVA) is a statistical method that studies the properties of extreme values of datasets, crucial for fields like engineering, meteorology, finance, insurance, and environmental science. EVA models extreme events using distributions such as Fr\'echet, Weibull, or Gumbel, aiding in risk prediction and management. This review explores EVA's application to nanoscale biological systems. Traditionally, biological research focuses on average values from repeated experiments. However, EVA offers insights into molecular mechanisms by examining extreme data points. We introduce EVA's concepts with simulations and review its use in studying motor protein movements within cells, highlighting the importance of in vivo analysis due to the complex intracellular environment. We suggest EVA as a tool for extracting motor proteins' physical properties in vivo and discuss its potential in other biological systems. While there have been only a few applications of EVA to biological systems, it holds promise for uncovering hidden properties in extreme data, promoting its broader application in life sciences.